A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China

• Published in: Remote sensing of environment Vol. 154; pp. 1 - 7
• Main Authors: Song, Weize, Jia, Haifeng, Huang, Jingfeng, Zhang, Yiyue
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 01.11.2014; Elsevier
• Subjects: Aerosol optical depth; Animal, plant and microbial ecology; Applied geophysics; Argon oxygen decarburizing; Biological and medical sciences; Brackish; China; Correlation; data collection; Deltas; Earth sciences; Earth, ocean, space; Exact sciences and technology; Freshwater; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Geographically weighted regression; Internal geophysics; particulates; Pearl River Delta region; PM2.5; Regional; Regression; regression analysis; Relative humidity; remote sensing; river deltas; Rivers; Satellite remote sensing; satellites; statistical models; Teledetection and vegetation maps; Pearl River; Far East; Asia; China; China, People's Rep., Zhu R; China, People's Rep; Satellite remote sensing; Aerosol optical depth; Pearl River Delta region; Geographically weighted regression; PM2.5; Relative humidity; accuracy; variability; Modeling; evaluation; spatial variations; Semiempirical method; deltas; performances; Linear regression; Estimation; Space remote sensing; Regional scope; satellite measurements; Satellite observation; concentration; Meteorological variable; corrections; Regression model; Statistical model; PM

To estimate the daily concentration of ground-level PM2.5 coincident to satellite overpass at regional scale, a satellite-based geographically weighted regression (GWR) model was developed. The model enhances PM2.5 estimation accuracy by considering spatial variation and nonstationarity that might i...